Credible plan-driven RAG method for Multi-hop Question Answering

• URL: http://arxiv.org/abs/2504.16787v1
• Date: Wed, 23 Apr 2025 15:03:17 GMT
• Title: Credible plan-driven RAG method for Multi-hop Question Answering
• Authors: Ningning Zhang, Chi Zhang, Zhizhong Tan, Xingxing Yang, Weiping Deng, Wenyong Wang,
• Abstract summary: Multi-hop question answering (QA) presents a considerable challenge for Retrieval-Augmented Generation (RAG)<n> deviations in reasoning paths or errors in intermediate results, which are common in current RAG methods, may propagate and accumulate throughout the reasoning process.<n>We propose the Plan-then-Act-and-Review (PAR RAG) framework, which is organized into three key stages: planning, act, and review.
• Score: 2.5772544412212985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-hop question answering (QA) presents a considerable challenge for Retrieval-Augmented Generation (RAG), requiring the structured decomposition of complex queries into logical reasoning paths and the generation of dependable intermediate results. However, deviations in reasoning paths or errors in intermediate results, which are common in current RAG methods, may propagate and accumulate throughout the reasoning process, diminishing the accuracy of the answer to complex queries. To address this challenge, we propose the Plan-then-Act-and-Review (PAR RAG) framework, which is organized into three key stages: planning, act, and review, and aims to offer an interpretable and incremental reasoning paradigm for accurate and reliable multi-hop question answering by mitigating error propagation.PAR RAG initially applies a top-down problem decomposition strategy, formulating a comprehensive plan that integrates multiple executable steps from a holistic viewpoint. This approach avoids the pitfalls of local optima common in traditional RAG methods, ensuring the accuracy of the entire reasoning path. Subsequently, PAR RAG incorporates a plan execution mechanism based on multi-granularity verification. By utilizing both coarse-grained similarity information and fine-grained relevant data, the framework thoroughly checks and adjusts intermediate results, ensuring process accuracy while effectively managing error propagation and amplification. Experimental results on multi-hop QA datasets demonstrate that the PAR RAG framework substantially outperforms existing state-of-the-art methods in key metrics, including EM and F1 scores.

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